基于知识和关系意识的协同学习,用于药物重新定位
IEEE journal of biomedical and health informatics
|April 29, 2025
概括
克兰通过改进药物发现的知识图分析来增强药物重新定位. 这种关系意识方法更好地捕捉了药物疾病相互作用,并克服了训练中的噪音,以实现更稳定的优化.
科学领域:
- 药理学和药物发现
- 生物信息学和计算生物学
- 人工智能在医学中的应用
背景情况:
- 药物重新定位通过确定现有药物的新用途来加速药物发现.
- 目前的知识图 (KG) 方法与复杂的药物-疾病关系和协同作用机制作斗争.
- 图形神经网络 (GNN) 和知识图嵌入 (KGE) 方法面临着噪音和不稳定的优化挑战.
研究的目的:
- 引入KRANE,一种基于知识和关系意识的协同学习方法,用于药物重新定位.
- 解决目前基于KG的药物重新定位方法的局限性,特别是捕捉复杂的关系和协同效应.
- 通过在训练期间减轻噪音来提高药物重新定位模型的稳定性和准确性.
主要方法:
- 开发了一个关系感知特征提取器 (RAFE),使用上下文三倍注意力得分来增强复杂的关系特征的表示.
- 实施了协同特征重建模块,以提取药物和疾病之间的协同异质特征相互作用.
- 提出了知识调节损失函数,以尽量减少噪声对模型训练的影响,并优化性能.
主要成果:
- 在三个公共数据集中,KRANE显示了与现有的药物重新定位方法相比的显著改进.
- 关系意识的方法有效地捕获了复杂的药物-药物,药物-疾病和疾病-疾病的关系.
- 协同的特征提取成功地确定了药物和疾病之间的未被探索的机制.
结论:
- 克兰通过增强知识图分析,为药物重新定位提供了一种有效和强大的方法.
- 该方法成功地解决了药物发现现有GNN和KGE技术的局限性.
- 克兰为确定现有药物的新治疗途径提供了一个稳定而准确的框架.
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